Fisher's Exact Test Analysis. — Analyze

Analyzes count data using the Fisher's exact test.

More information can be found in The Fisher's Exact Method Section of the KRI Method vignette.

Usage

Analyze_Fisher(dfTransformed, strOutcome = "Numerator")

Arguments

dfTransformed: data.frame Transformed data for analysis. Data should have one record per site with expected columns: GroupID, GroupLevel, Numerator, Denominator, and Metric. For more details see the Data Model vignette: vignette("DataModel", package = "gsm"). For this function, dfTransformed should typically be created using Transform_Rate().
strOutcome: character required, name of column in dfTransformed dataset to perform Fisher's exact test on. Default is "Numerator".

Value

data.frame with one row per site with columns: GroupID, Numerator, Numerator_Other, Denominator, Denominator_Other, Prop, Prop_Other, Metric, Estimate, and Score.

Statistical Methods

The function Analyze_Fisher utilizes stats::fisher.test to generate an estimate of odds ratio as well as a p-value using the Fisher’s exact test with site-level count data. For each site, the Fisher’s exact test is conducted by comparing the given site to all other sites combined in a 2×2 contingency table. The p-values are then used as a scoring metric in {gsm} to flag possible outliers. The default in stats::fisher.test uses a two-sided test (equivalent to testing the null of OR = 1) and does not compute p-values by Monte Carlo simulation unless simulate.p.value = TRUE. Sites with p-values less than 0.05 from the Fisher’s exact test analysis are flagged by default. The significance level was set at a common choice.

Examples

dfTransformed <- Transform_Rate(
  analyticsInput
)
dfAnalyzed <- Analyze_Fisher(dfTransformed)